Sub-optimal iterative receiver method and system for a high-bit-rate cdma transmission system

ABSTRACT

The method consists in estimating the transmission channel using received predefined pilot symbols, reconstituting the transmitted signal using the channel estimate on the basis of received signals that were transmitted via multiple paths of the transmission channel, determining from the channel estimate an equivalent model of the channel as seen from the reconstituted signal, reducing interference between symbols of the reconstituted transmitted signal resulting from a low spreading factor using the channel equivalent model and a DDFSE detector based on a trellis with a reduced number of states and delivering at its output estimated values of the received coded symbols, de-interleaving the coded symbols, and then decoding the estimated values of the de-interleaved coded symbols to reconstitute the transmitted data symbols.

The present invention relates to a high-bit-rate radio receiver methodand system for receiving signals in a radio system utilizing the codedivision multiple access (CDMA) technique.

It applies in particular, although not exclusively, to high-bit-rate (atleast 2 megabits per second (Mbit/s)) mobile telephone systems such asthe European Universal Mobile Telecommunication System (UMTS) designedto offer a wide range of services having different bit rates andspreading factors. The services include a high-speed downlink packetaccess (HSDPA) mode of transmission to mobile terminals that ischaracterized by dispensing with closed loop power control and linkadaptation utilizing variable constellation modulation (QPSK, MAQ16,MAQ64) and a low spreading factor.

Information is transmitted in mobile telephone systems using a multipleaccess technique. Some systems use frequency division multiple access(FDMA) and time division multiple access (TDMA), users of the networkbeing distinguished from each other by the respective frequency used andby the information to be transmitted being delivered in time slotsassigned to each user. In systems based on code division multiple access(CDMA), users communicate with each other using the same radio frequencyband. To be able to distinguish users from one another, each is assigneda respective spreading code for the whole duration of a connection, thecode being used to spread the spectrum of the signal to be transmittedin base band. To reconstitute the information transmitted, the receiversmust use the same code to effect the operation that is the converse ofthe spreading operation. Compared to other multiple access methods, thistechnique has the advantage of being more flexible in terms of accessand bit rate, which can be varied by altering the spreading factor.

In radio transmission, the form of the medium between the sender and thereceiver of a radio signal interferes with transmission and leads topropagation along multiple paths caused by reflections at differentpoints along the radio channel, especially in an urban environment. As aresult, components of the same signal reach the receiver with differentpowers and different time delays.

CDMA receiver systems use a rake receiver to reconstitute thetransmitted signal from the components received over differentpropagation paths. These receivers are based on reconstituting a delayprofile or radio channel equivalent model. To this end, a sequence ofpilot symbols known to the receivers is transmitted together with theinformation, and on the basis of this prior knowledge the receiversperform an estimation (an impulse response representing all the paths ofthe radio channel) of the radio channel over which the received signalwas transmitted. A matched filter is shifted over the received signal,for example by half a spreading code unit, while the received power ismeasured. This technique is used to construct an impulse response graphgiving information on the power and the time delays caused by multipathpropagation of the components of the signal received over a given radiochannel.

Although the CDMA technique would seem to be very suitable for real timelow-bit-rate services, it appears to be unsuitable for high-bit-ratepacket services because the performance of the rake receiver is based oncross-correlation and autocorrelation properties of the spreadingsequences, which improves as the length of the spreading sequence, andthus of the spreading factor, is increased. Now, the higher the bitrate, the lower the spreading factor. The spreading sequence becomesshorter and the cross-correlation and autocorrelation properties of thespreading sequences are therefore degraded, leading to interferencebetween symbols of the same transmitted signal. As a result of this, theperformance of the rake receiver is seriously degraded for spreadingfactors less than 8, especially if the type of modulation used has alarge number of states.

A study of the degraded performance of rake receivers caused byintersymbol interference has shown that it is necessary to use anequalization technique if the spreading factor is less than 16 (see [1]“On the rake receiver performance” H. Boujemaa, M. Siala, VTC 2000 Fall,Boston, USA).

Thus rake receivers on their own are found to be a very unsuitableresponse to the requirements of high-bit-rate mobile telephony.

At present it is virtually impossible to use optimum detection andencoding techniques, as this leads to very high calculation complexity,especially if the transmission channels have an impulse response that istoo long, as is the case in an urban environment.

Various suboptimal detection and decoding methods are used in the timedivision multiple access (TDMA) technique.

For example, the technique using a linear minimum mean square errorequalizer (LMMSE) reduces not only intersymbol interference but alsointeruser interference. For more, details, see the documents:

-   -   [2] “Linear receivers for the DS-CDMA downlink exploiting        orthogonality of spreading sequences” by I. Ghauri and D. T. M.        Slock, in Proc. 32nd Asilomar Conf. on Signals, Systems and        Comp., Asilomar, Calif., Nov. 1-4 1998, and    -   [3] “Interference Suppression in CDMA Downlink through Adaptive        Channel Equalization”, by M. Heikkilä, P. Komulainen, and J.        Lilleberg, in VTC 99 Fall, Tokyo, Japan.

Note that interuser interference on downlinks is caused by multipathpropagation in the channels, given that the spreading sequences ofdifferent users are mutually orthogonal. That solution proves to be veryeffective at reducing interuser interference, compared to rakereceivers. However, because of the linear characteristics of the LMMSEequalizer, that solution does not significantly reduce intersymbolinterference.

It has also been proposed to provide a maximum likelihood sequenceestimation equalizer (MLSE) at the output of a rake receiver. For moredetails of that technique see for example the document [4] “Jointmultipath combining and MLSE equalization (rake-MLSE Receiver) for WCDMAsystems” by S. Tantikovit, and A. U. H. Sheikh, in VTC 2000 Spring,Tokyo, Japan.

That solution is optimized from the sequence-detection point of view,and close to the optimum solution in terms of detecting errors in thesymbols transmitted. However, the complexity of that solution increasesexponentially with the spreading of the time delays in the same channeland with the size of the modulation constellation employed. Thus itcannot be applied to all UMTS services. Furthermore, that solution doesnot take into account the degraded performance caused by incorrectchannel estimation and channel coding. Furthermore, it does not offer aflexible or weighted output algorithm.

The prior art delayed decision feedback sequence estimation (DDFSE)technique reduces the complexity of the states of the trellis by usingthe “per survivor” processing technique. For more details of thattechnique, see for example the document [5] “Delayed Decision-FeedbackSequence Estimation” by A. Duel-Hallen and C. Heegard, in IEEETransactions on Communications, Vol. 37, pp. 428-436, May 1989.

In TDMA systems, that technique has the drawback of being sensitive toerror propagation, which necessitates prefiltering. That techniqueappears to be inapplicable to CDMA systems since the channel equivalentmodel at the rake receiver output varies on each symbol transmitted, asit depends on the spreading code, which changes on each symbol.

An iterative detection and decoding method designed for the TDMAtechnique and known as “turbo-detection” is described in the paper [6]“Iterative Correction of Intersequential Interference:Turbo-equalization” by C. Douillard, M. Jezequel, C. Berrou, A. Picart,P. Didier and A. Glavieux, published in European Transactions onTelecommunications, Vol. 6, p. 507 to 511, September 1995. In thatdetection and decoding technique, an MLSE equalizer with weighted inputsand outputs (SISO MLSE) is used, and the decoding process is of theViterbi type, also with weighted inputs and outputs (SOVA). That processis described in a paper [7] entitled “A low Complexity Soft OutputViterbi Decoder Architecture”, ICC '93 p. 733 to 740, Geneva,Switzerland, May 1993.

The above detection and decoding technique has been further developed,yielding optimized maximum a posteriori probability (MAP) detectors. Formore details of those detectors, see the following papers:

-   -   [8] “Optimal Decoding of Linear Codes for Minimizing Symbol        Error Rate” published by L. R. Bahl, J. Cocke, F. Jelinek and J.        Raviv in IEEE Transactions on Information Theory, Vol. IT-20, p.        284-287, March 1994; and    -   [9] “Iterative Equalization and Decoding in Mobile        Communications Systems”, published by G. Baush, H. Khorram        and J. Hagenauer in Proc. EPMCC '97, p. 307-312, Bonn, Germany,        September 1997.

However, the above solution has not been transposed to CDMA systems, andfurther introduces undue complexity of the receiver, of the order ofM^(L), where M is the number of points of the modulation constellationand L is the number of echoes in the propagation channel taken intoaccount. Moreover, it does not address the channel estimation problem.

Finally, the paper [10] “Turbo-Equalization over Frequency SelectiveChannel”

-   -   International Symposium on Turbo-Codes, Brest, France, September        1997, proposes an iterative symbol detection and channel        decoding technique, known as “turbo-equalization” and        significantly different from the turbo-detection technique        mentioned above, and which presupposes a noisy estimation of the        transmission channel. However, compared to the turbo-detection        technique, the turbo-equalization technique degrades performance        in a way that is strongly dependant on the equalization        technique employed for the first iteration. On that subject, see        the paper [11] “Joint Equalization and Decoding: Why Choose the        Iterative Solution?” by A. Roumy, I. Figalkow and D. Pirez, in        IEEE VTC '1999 Fall, Amsterdam, Netherlands, September 1999.

That technique cannot be transposed to CDMA systems since it is based onfiltering techniques that cannot be applied if the channel variesindependently from one transmitted symbol to another.

An object of the present invention is to solve these problems in orderto propose a reception method and a receiver structure that arerelatively simple, that have close to optimum performance, and that usehigh order modulation combined with a low spreading factor. This objectis achieved by providing a method of receiving a signal transmitted on amultipath transmission channel using a spread spectrum technique with alow spreading factor, said signal being transmitted in the form ofsequences of coded binary symbols comprising both predefined pilotsymbols and data symbols multiplied by a spreading sequence, said methodincluding a step of determining a channel estimate using receivedpredefined pilot symbols.

According to the invention, the method comprises the steps of:

-   -   reconstituting the transmitted signal using the channel estimate        on the basis of received signals that were transmitted over the        multiple paths of the transmission channel,    -   determining from the channel estimate an equivalent model of the        channel as seen from the reconstituted signal,    -   reducing intersymbol interference in the reconstituted        transmitted signal resulting from a low spreading factor, using        the channel equivalent model and a DDFSE detector based on a        trellis with a reduced number of states and delivering at its        output estimated values of the received coded symbols,    -   de-interleaving the coded symbols, and    -   decoding the estimated values of the de-interleaved coded        symbols to reconstitute the transmitted data symbols.

The estimated values of the coded symbols received obtained afterinterference reduction and after decoding are advantageously weighted orflexible values.

According to a feature of the invention, the steps of reducingintersymbol interference and of decoding are included in an iterativeprocess wherein the de-interleaved coded symbols obtained in aniteration n are re-estimated during decoding as a function of the datasymbols obtained after decoding and error correction, the differencebetween the re-estimated coded symbols obtained in the same iterationand the de-interleaved coded symbols obtained in the next iteration n+1is re-interleaved and then applied to the input of the DDFSE detectorand subtracted from the coded symbols obtained in the iteration n+1 atthe output of the DDFSE detector.

The channel estimate is advantageously improved using the least squares(LS) method.

The channel estimate is preferably improved using the minimum meansquare error (MMSE) algorithm.

According to another feature of the invention, the channel is estimatedin an iterative process wherein the coded and de-interleaved symbolsobtained in an iteration n are re-estimated during decoding, as afunction of the data symbols obtained after decoding and errorcorrection, the re-estimated coded symbols being interleaved, a channelestimate being obtained on the basis of the re-estimated and interleavedcoded symbols, an equivalent channel model being determined from thechannel estimate, and the channel estimate and the channel equivalentmodel determined in an iteration n being respectively used toreconstitute the transmitted signal and to reduce intersymbolinterference in the next iteration n+1.

The invention also provides a system for receiving a signal transmittedon a multipath transmission channel using a spread spectrum techniqueand a low spreading factor, said signal being transmitted in the form ofa sequence of coded binary symbols comprising predefined pilot symbolsand data symbols and multiplied by a spreading sequence, said systemcomprising a rake receiver for reconstituting the transmitted signalusing a channel estimate on the basis of the signals received andtransmitted by the multiple paths of the transmission channel andchannel estimation means for estimating the channel on the basis of thepilot symbols received, to deliver a transmission channel estimate tothe rake receiver.

According to the invention, the system further comprises:

-   -   channel modeling means for determining an equivalent model of        the channel as seen at the output of the rake receiver as a        function of the channel estimate,    -   reduction means for reducing intersymbol interference between        received symbols, comprising a DDFSE detector based on a trellis        with a reduced number of states, for reducing intersymbol        interference between received symbols using the equivalent        channel model and reconstituting estimated values of the coded        symbols received,    -   de-interleaving means for de-interleaving the estimated values        of the received coded symbols, and    -   decoding means for decoding the estimated and de-interleaved        values and supplying the transmitted data symbols.

The estimated and decoded values delivered by the intersymbolinterference reduction means and the decoding means are advantageouslyweighted or flexible values.

According to a feature of the invention, the system further comprises:

-   -   means for re-estimating the coded symbols as a function of the        decoded data symbols after error correction,    -   first subtraction means for subtracting the estimated and        de-interleaved coded symbols from the re-estimated coded symbols        and obtaining a sequence of extrinsic re-estimated coded        symbols,    -   first interleaving means for interleaving the sequence of        extrinsic re-estimated coded symbols, and    -   second subtraction means for subtracting the sequence of        extrinsic re-estimated coded symbols from the sequence of        symbols received and estimated by the reduction means on the        next iteration.

According to another feature of the invention, the system furthercomprises:

-   -   second interleaving means for interleaving the sequence of        re-estimated coded symbols at the output of the decoding means,        and    -   second channel estimation means for supplying a transmission        channel estimate on the basis of the interleaved sequence of        re-estimated coded symbols to the means for determining an        equivalent channel model and to the rake receiver.

A preferred embodiment of the invention is described hereinafter by wayof non-limiting example and with reference to the accompanying drawings,in which:

FIG. 1 is a diagram showing a conventional transmitter designed totransmit signals using the CDMA technique;

FIG. 2 is a diagram showing a reception system of the invention;

FIGS. 3 and 4 are diagrams showing two preferred variants of thereception system shown in FIG. 2;

FIGS. 5 and 6 are curves of the bit error rate as a function of thesignal-to-noise ratio illustrating the performance of the receiversshown in FIGS. 2 to 4; and

FIG. 7 is a diagram showing an embodiment of a channel estimator of theinvention.

FIG. 1 shows a prior art CDMA transmitter, comprising a signal source 1supplying sequences of τ₀ binary symbols U₁ ^(τ) ⁰ ={u₁, . . . , u_(τ) ₀}^(T) and a channel encoder 2 that supplies a coded sequence c₁ ^(τ) ⁰={c₁, . . . , c_(τ) ₀ }^(T).

Each data symbol U_(n)={U_(n,1), . . . , U_(n,k) ₀ )^(T) contains k₀bits and each symbol c_(n)={c_(n,1), . . . , c_(n,n) ₀ }^(T) contains n₀bits. The coded bits are interleaved by an interleaver 3 and padded outto correspond to a predetermined transmission format, i.e. frames oflength τ containing pilot symbols to allow a receiver to carry outchannel estimation. The resulting bits are grouped into symbols of typea_(k)=(a_(k,l), . . . , a_(k,q)) containing q bits before they are fedto a modulator 4 performing M-th order phase-shift keying (M-PSK), MAQ16or MAQ64, which supplies a corresponding modulated symbol s(k).

The signal s(k) is then multiplied at 5 by a predefined spreadingsequence c(q) for the transmission in question, the resulting signalbeing passed through a raised cosine square root filter 6 (Nyquist rootfilter), with a spectral occupancy factor (roll-off) of 0.22.

In FIG. 2, the receiver of the invention comprises a rake receiver 11that utilizes a channel estimate supplied by a channel estimator 10 andis followed by an intersymbol interference reduction device 12 to whichan equivalent model 16 of the transmission channel is fed. The output ofthe interference reduction device 12 is connected to a decoder 15 via ade-interleaver 14 carrying out the operation that is the converse of theinterleaving operation 3. Moreover, the channel estimate supplied by theestimator 10 is applied to the channel model 16 to determine a channelequivalent model.

According to the invention, the intersymbol interference reductiondevice 12 is of the delayed decision feedback sequence estimation(DDFSE) type with weighted input and output, i.e. takes the form of alogarithm ln[p(1)/p(0)] of the ratio of probabilities p(1) and p(0) thatthe signal level is respectively logic level 1 and logic level 0.Similarly, the decoder 15 also has weighted inputs and outputs.

In the case of multipath propagation, the signal received at the inputof the spread spectrum receiver at time t takes the form:$\begin{matrix}{{r(t)} = {{\sum\limits_{l = 1}^{L}{{h_{l}(t)}\quad{\sum\limits_{k}{{s(k)}\quad{e_{k}\left( {t - {kT}_{s} - {\tau_{l}(t)}} \right)}}}}} + {w(t)}}} & (1)\end{matrix}$in which e_(k)(t)=Σ_(q=0) ^(N−1)e(kN+q)g(t−qT_(c)) is the waveform forthe modulated symbol s(k), e(q) is the spreading sequence, N is thespreading factor, g(t) is the transfer function of the Nyquist rootfilter 6, T_(e) and T_(s), are respectively the “chip” and symbolperiods, L is the number of paths in the channel, h_(l)(t) and τ_(l)(t)are respectively the complex amplitude and the time delay of the l-thpath, and ω(t) is Gaussian white noise with a power spectral density N₀.

If d(i) is the product of the transmitted symbols and multiplied by thespreading sequence,${{c(i)}{s\left( \left\lfloor \frac{i}{n} \right\rfloor \right)}},$where └ ┘ represents the “integer part” function.

The received signal can also be written in the following form:$\begin{matrix}{{r(t)} = {\sum\limits_{i}{{d(i)}\quad{h^{i}\left( {t - {i\quad T_{c}}} \right)}}}} & (2)\end{matrix}$in which: $\begin{matrix}{{h^{i}(t)} = {\sum\limits_{l = 1}^{L}{{h_{l}\left( {t + {i\quad T_{c}}} \right)}\quad{g\left( {t - {\tau_{l}\left( {t + {i\quad T_{c}}} \right)}} \right)}}}} & (3)\end{matrix}$

In accordance with Nyquist's theorem, the received signal r(t) issampled at twice the chip frequency to obtain a vector r(i) of stackedsamples, which is used to estimate the chip symbol i.

The vector r(i) takes the following form: $\begin{matrix}{{r(i)} = {\begin{pmatrix}{r\left( {\left( {i - M_{1}} \right)T_{c}} \right)} \\{r\left( {{\left( {i - M_{1}} \right)T_{c}} + {T_{c}/2}} \right)} \\\vdots \\{r\left( {\left( {i + M_{2}} \right)T_{c}} \right)}\end{pmatrix} = {{{H(i)}{d(i)}} + {w(i)}}}} & (4)\end{matrix}$in which M₁ and M₂ represent the length of h^(i)(t) as a multiple ofT_(e),H(i)=[h ^(M) ¹ ^(+M) ² (i), . . . , h ⁰(i), h ₁(i), h _(M) ₁ _(+M) ₂(i)]  (5)h ^(j)(i)=[h ^(i)((j−M ₁)T _(c)), h ^(i)((j−M ₁)T _(c) +T _(c)/2), . . ., h ^(i)(M ₂ T _(c)), 0_(1,2j)]^(T), 0≦j≦M ₁ +M ₂,   (6)h _(j)(i)=[0_(1,2j) , h ^(i)(−M ₁ T _(c)),h ^(i)(−M ₁ T _(c) +T _(c/)2),. . . , h ^(i)((M ₂ −j)T _(c))]^(T), 0≦j≦M ₁ +M ₂,   (7)andd(i)=[d(i−M ₁ −M ₂), . . . , d(i), . . . , d(i+M ₁ +M ₂)]^(T)   (8)

After the operation that is the converse of the spreading operation, thereduced signal for the symbol k on the branch i (path i of thetransmission channel) can be written as follows: $\begin{matrix}{{z_{k}\left( \tau_{j} \right)} = {{s_{k}h_{j}} + {\sum\limits_{i \neq j}{h_{i}\quad{\sum\limits_{n = {kN}}^{{{({k + 1})}N} - 1}{e_{n}^{*}d_{n + \tau_{ji}}}}}} + {w_{k}\left( \tau_{j} \right)}}} & (9)\end{matrix}$in which d_(k) is the product of the spreading sequence and the symbolstransmitted and τ_(ji)=(τ_(j)−τ_(i))/T_(c).

Using the results reported in document [1], it can easily be shown thatthe output ô_(k) of the rake receiver 11 can be represented as follows:$\begin{matrix}{{\hat{o}}_{k} = {{\sum\limits_{j = 1}^{L}{h_{j}^{*}{z_{k}\left( \tau_{j} \right)}}} = {{\sum\limits_{l = {- L^{\prime}}}^{L^{\prime}}{{g_{l}(k)}\quad s_{k - l}}} + w_{k}}}} & (10)\end{matrix}$in which: $\begin{matrix}{w_{k} = {\sum\limits_{j = 1}^{L}{h_{j}^{*}\quad{w_{k}\left( \tau_{j} \right)}}}} & (11)\end{matrix}$g_(l)(k) is the l-th amplitude of the equivalent model at the output ofthe rake receiver 11, and (2L′+1) is the number of echoes of theequivalent model 16.

Assuming that the path delays are spaced by a multiple of the chipperiod T_(c), the parameters of the equivalent model 16 are given by thefollowing equations: $\begin{matrix}{L^{\prime} = {1 + {\max\left\{ \left\lbrack \frac{i}{n} \right\rbrack \right\}}}} & (12) \\{{g_{0}(k)} = {{\sum\limits_{j = 1}^{L}{h_{j}}^{2}} + {\sum\limits_{{- 1} < \frac{\tau_{ji}}{N} < 1}{h_{j}^{*}h_{i}\quad{\sum\limits_{n = {ɛ_{ij}^{-}{(k)}}}^{ɛ_{ij}^{+}{(k)}}{e_{n}^{*}\quad e_{n + \tau_{ji}}}}}}}} & (13) \\{{g_{l}(k)} = {{\sum\limits_{{l - 1} < \frac{\tau_{ji}}{N} \leq l}{h_{j}^{*}\quad h_{i}\quad{\sum\limits_{n = {kN}}^{{kN} - \tau_{ji} - 1 - {{({l - 1})}N}}{e_{n}^{*}e_{n + \tau_{ji}}}}}} +}} & (14) \\{\quad\begin{matrix}{{\sum\limits_{l < \frac{\tau_{ji}}{N} < {l + 1}}{h_{j}^{*}\quad h_{i}\quad{\sum\limits_{n = {{kN} - \tau_{ji} - {l\quad N}}}^{{{({k + 1})}N} - 1}{e_{n}^{*}e_{n + \tau_{ji}}}}}},} & {{\forall{1 \leq l \leq L}},}\end{matrix}} & \quad \\{{g_{- l}(k)} = {{\sum\limits_{{- l} < \frac{\tau_{ji}}{N} \leq {- {({l - 1})}}}{h_{j}^{*}\quad h_{i}\quad{\sum\limits_{n = {{{({k + 1})}N} - \tau_{ji} + {{({l - 1})}N}}}^{{{({k + 1})}N} - 1}{e_{n}^{*}e_{n + \tau_{ji}}}}}} +}} & (15) \\{\quad\begin{matrix}{{\sum\limits_{{{- l} - 1} < \frac{\tau_{ji}}{N} < {- l}}{h_{j}^{*}\quad h_{i}\quad{\sum\limits_{n = {kN}}^{{{({k + 1})}N} - \tau_{ji} + {l\quad N}}{e_{n}^{*}e_{n + \tau_{ji}}}}}},} & {{\forall{1 \leq l \leq L}},}\end{matrix}} & \quad\end{matrix}$ε_(ij) ⁻(k)=max (kN−τ _(ji) , kN)   (16)andε_(ij) ³⁰ (k)=min ((k+1)N−τ _(ji), (k+1)N−τ _(ji), (k+1)N)   (17)

The FIG. 5 curves give the bit error rate BER as a function of thesignal-to-noise ratio Eb/N₀ in the ideal case (curve C1) and at the rakereceiver output (curve C2).

These curves, and those of FIG. 6, were obtained by simulation with aspreading factor of 4, an EQ-4 transmission channel with four paths withrespective delays separated by a chip period, each path having acircular complex Gaussian shape or being subject to Rayleighattenuation. The output code is a recursive systematic code with 16states, of ratio 1/2 and with generation polynomials$\left( {1,\frac{1 + D^{3} + D^{4}}{1 + D + D^{4}}} \right)$generating a pre-encoded sequence c that is sent to a pseudo-randominterleaver and divided into frames.

Comparing the curves C1 and C2 shows that the performance of this kindof receiver is very poor.

If, as recommended in references [2] and [3], an LMMSE equalizer isplaced upstream of the rake receiver, to reduce the degradation causedby intersymbol interference resulting from a low spreading factor, theestimate of the i-th chip symbol takes the following form:$\begin{matrix}{{\hat{d}(i)} = {{h^{0}(i)}^{H}\left( {{{H(i)}{H(i)}^{H}} + {\frac{N_{0}}{2\sigma_{d}^{2}}I_{{2{({M_{1} + M_{2}})}} + 1}}} \right)^{- 1}{r(i)}}} & (18)\end{matrix}$in which σ_(d) ² is the variance of the chip sequence.

The curve C3 in FIG. 5, which shows the performance obtained with thiskind of equalizer, shows that this solution does not significantlyimprove the performance of the rake receiver.

To improve significantly the performance of the rake receiver 11 of theinvention, there is placed at its output an intersymbol interferencereduction device 12 designed around a suboptimal DDFSE detector based ona trellis with a reduced number of states, this kind of detector using achannel equivalent model 16.

Before they are applied to the DDFSE detector, the samples are delayedto render the channel equivalent model 16 causal. They can then berepresented as follows: $\begin{matrix}{y_{k} = {{z^{- L^{\prime}}{\hat{o}}_{k}} = {{\sum\limits_{l = 0}^{2L^{\prime}}{{\underset{\_}{h_{l}}(k)}s_{k - l}}} + w_{k}}}} & (19)\end{matrix}$in which the vector h _(l)(k)=g_(l−L′)(k), this vector representing thevector of the channel coefficients [h ₀(k), . . . , h _(2L′)(k)]^(T).

The DDFSE detector then operates on a trellis with a reduced number ofstates (see document [8]), compared to the BCJR technique which appliesa maximum a posteriori probability (MAP) criterion to a complete trelliswith Q^(2L′) states, where Q is the number of points of the PSKmodulation constellation and (2L′+1 ) is the number of paths of thechannel equivalent model 16.

This kind of trellis is in fact a finite state machine, spread in timeand in which transitions between states depend only on the precedingstate, the number of states at each instant being constant. In this kindof trellis, a section represents all of the transitions between thestates corresponding to two successive instants.

In the DDFSE detector, the trellis is therefore reduced to a number ofstates Q^(ν) ^(r) , where ν_(r) is a positive integer called the reducedmemory and selected so that or ν_(r)<2L′ in the case of the DDFSEdetector.

A trellis input sequence a₁ ^(n) is generally said to terminate with asubstate s if a₁ ^(n) terminates with the substring s=a_(nν) _(r) ₊₁^(n).At a depth n, the substates space S_(n) coincides with the completespace S_(n) of the states of the BCJR trellis if ν_(r)=2L′. Ifν_(r)<2L′, S_(n) is reduced to a subset comprising all possiblesubstates s derived from all the states:|S _(n) |=Q ^(ν) ^(r) ,∀nε[0,τ]et |B _(n) |=Q ^(ν) ^(r) ⁺¹,∀nε[1,τ]  (20)

The notation employed in the above equations applies to the definitionof a subtrellis T(S, B) to which the DDFSE algorithm is applied.

In each section, and for all transitions, the branch metric calculationimplies convolution of the impulse response in discrete time of thechannel with the sequence of 2L′+1 symbols already estimated. Only thefirst ν_(r)+1 symbols estimated for this sequence are available at thetransition being processed and at the starting subtrellis substate withwhich it is connected.

At each temporal index nε[1,τ] and for all the bit indices jε[1,q], theoptimum symbol by symbol BCJR algorithm supplies the logarithms of the aposteriori probability ratios, in accordance with the followingequation: $\begin{matrix}{{\lambda\left( a_{n,j} \right)} = {\ln\frac{\Pr\left( {{a_{n,j} = \left. 1 \middle| y_{1}^{\tau} \right.},{\underset{\_}{\hat{h}}(n)}} \right)}{\Pr\left( {{a_{n,j} = \left. 0 \middle| y_{1}^{\tau} \right.},{\underset{\_}{\hat{h}}(n)}} \right)}}} & (21)\end{matrix}$in which ĥ is an estimate (or re-estimate) of the transverse vector ofthe channel coefficients (if possible converted for minimum phase), andy₁ ^(τ) is an observed sequence of length τ. In the followingderivation, the conditioning by ĥ is implicit and omitted to simplifythe expressions.

If marginalization is applied to the marked bit input symbol sequences,the equation (21) can be re-written in the following form:$\begin{matrix}{{\lambda\left( a_{n,j} \right)} = {\ln\frac{\sum\limits_{a_{1}^{\tau},{a_{n,j} = 1}}{p\left( {a_{1}^{\tau},y_{1}^{\tau}} \right)}}{\sum\limits_{a_{1}^{\tau},{a_{n,j} = 0}}{p\left( {a_{1}^{\tau},y_{1}^{\tau}} \right)}}}} & (22)\end{matrix}$in which p(a₁ ^(τ), y₁ ^(τ))=Pr(y₁ ^(τ)=y₁ ^(τ)|a₁ ^(τ)) Pr(a₁ ^(τ)=a₁^(τ)).

Making the following Min-Log-BCJR approximation: $\begin{matrix}{{- {\ln\left( {\sum\limits_{k}^{\quad}{\exp\left( {- \Delta_{k}} \right)}} \right)}} \simeq {\min\limits_{k}\quad\Delta_{k}}} & (23)\end{matrix}$where Δ_(k) represents non-negative quantities, the logarithm λ(a_(nj))of the a posteriori probability ratio can be evaluated using thefollowing formula: $\begin{matrix}{{\lambda\left( a_{n,j} \right)} \simeq {{\min\limits_{a_{1}^{\tau},{a_{n,j} = 0}}\left\{ {{- \ln}\quad{p\left( {a_{1}^{\tau},y_{1}^{\tau}} \right)}} \right\}} - {\min\limits_{a_{1}^{\tau},{a_{n,j} = 1}}\left\{ {{- \ln}\quad{p\left( {a_{1}^{\tau},y_{1}^{\tau}} \right)}} \right\}}}} & (24)\end{matrix}$in which {−lnp(a₁ ^(τ),y₁ ^(τ))} represents the metric cost,corresponding to the noise, of the path in the trellis, associated withinput sequence a₁ ^(n) and the received sequence y₁ ^(n). Because of thereduction of the trellis, the DDFSE device 12 evaluates the quantity{−lnp(a₁ ^(τ),y₁ ^(τ))} in a suboptimal manner on the basis of the “persurvivor” PSP algorithm that consists in selecting only one survivor pernode. For a given subtrellis T(S,B) and a particular metric branch, theexpression μ_(n) ^(⇄)(b) denotes the metric cost of the best pathbeginning at the substate 0 at the depth 0 and terminating at thesubstate 0 at the depth τ (taking account of the tailing symbols of thesequence), and passing through the branch bεB_(n) of the section n. Itis also assumed that each branch bεB_(n) contains three fields: a startsubstate field b⁻εS_(n−1), an arrival substate field b⁺εS_(n) and afield labeled b^(∇)={b₁ ^(∇), . . . ,b_(q) ^(∇)} modeling a bit labelinput symbol for the intersymbol interference convolutional code oflevel 1 varying as a function of time at the instant n. The output ofthe DDFSE device 12 can be represented as follows: $\begin{matrix}{{\lambda^{\prime}\left( a_{n,j} \right)} = {{\min\limits_{{b \in B_{n}},{b_{j}^{▼} = 0}}\quad{\mu_{n}^{\leftrightarrow}(b)}} - {\min\limits_{{b \in B_{n}},{b_{j}^{▼} = 1}}\quad{\mu_{n}^{\leftrightarrow}(b)}}}} & (25)\end{matrix}$

The metric cost μ_(n) ^(⇄)(b) considered in the preceding formula canalways be decomposed into a sum of three terms:μ_(n) ^(⇄)(b)=μ_(n−1) ^(→)(b ⁻)+ξ_(n) (b)+μ_(n) ^(←)(b ⁺)   (26)in which μ_(n) ^(→)(s) represents the accumulated forward metric of thebest subpath starting from the substate 0εS₀ and terminating at thesubstate sεS_(n), and is calculated recursively using the followingformula: $\begin{matrix}{{\mu_{n}^{\rightarrow}(s)} = {\min\limits_{{b \in B_{n - 1}},{b^{+} = s}}\left\{ {{\mu_{n - 1}^{\rightarrow}\left( b^{-} \right)} + {\xi_{n}(b)}} \right\}}} & (27)\end{matrix}$with the following limit conditions:μ₀ ^(→)(0)=0 and μ₀ ^(→)(s)=∞,∀s≠0   (28)and where μ_(n) ^(←)(s) represents the accumulated backward metric ofthe best subpath starting from the substate sεS_(n) and terminating atthe substate 0εS_(τ), and is calculated recursively using the followingformula: $\begin{matrix}{{\mu_{n}^{\leftarrow}(s)} = {\min\limits_{{b \in B_{n + 1}},{b^{-} = s}}\left\{ {{\mu_{n + 1}^{\leftarrow}\left( b^{+} \right)} + {\xi_{n + 1}(b)}} \right\}}} & (29)\end{matrix}$with the following limit conditions:μ_(τ) ^(→)(0)=0 and μ_(τ) ^(→)(s)=∞,∀s≠0  (30)

The branch metric ξ_(n)(b) based on the PSP algorithm and used by theDDFSE device 12 is expressed as follows: $\begin{matrix}{{\xi_{n}(b)} = {\frac{1}{2\sigma^{2}}{{y_{n} - {{{\underset{\_}{\hat{h}}}_{0}(n)}s_{n}} - {\sum\limits_{k = 1}^{v_{\tau}}{{{\underset{\_}{\hat{h}}}_{k}(n)}s_{n - k}}} - {\sum\limits_{k = {v_{r} + 1}}^{2L^{\prime}}{{{\underset{\_}{\hat{h}}}_{k}(n)}{\hat{s}}_{n - k}}}}}^{2}}} & (31)\end{matrix}$

In the above equation, the complex symbol s_(n) penetrating theintersymbol interference code at the time n results simply from theredefinition of the branch label b^(∇). The complex sequence of symbols{s_(n−ν) _(r) , . . . ,s_(n−1) } is simply deduced from the substate b⁻,while the estimated sequence of symbols {ŝ_(n−2L′), . . . , ŝ_(n−ν) _(r)⁻¹} is obtained by travelling backwards along the “survivor” path thatterminates at b⁻ and redefining the labels of the branches constitutingit. The “survivor” paths are assumed to be memorized in a slidingtraceback matrix of depth 2L′.

The curve C4 in FIG. 5 that shows the performance of this solution wasobtained with a DDFSE reduced trellis complexity of four states(ν_(r)=1). This curve shows that even with a low interleaving factor(=4), and a significant reduction in the number of states of thetrellis, this solution approximates the ideal solution, and inparticular the solution shown by the curve C5, which employs a maximumlikelihood sequence estimation (MLSE) type channel estimation, whichcannot be applied to the CDMA technique because of its complexity.

To improve further the performance of this kind of receiver, as shown inFIG. 3, the invention proposes to connect the output of the device 12 tothe positive input of a comparator 13 whose output is connected to thede-interleaver 14. The output of the de-interleaver 14 is connected toan input receiving the logarithm of the intrinsic probability ratio ofthe coded bits from the decoder 15 which supplies at a first output thelogarithm of a posteriori probability ratios on the data bitstransmitted and at a second output the logarithm of the a posterioriprobability ratio on the coded bits that is determined by an operationthat is the converse of the decoding applied to the decoded data bitsafter error correction. The second output of the decoder 15 is loopedvia a comparator 18 and an interleaver 17 to the negative input of thecomparator 13 and to an input of the DDFSE device 12. The output of thede-interleaver 14 is also fed to the negative input of the comparator18.

The decoder 15 can decode the channel code optimally using the BCJRalgorithm. The interference reduction device 12 delivers a posterioriprobability ratios on the value of the bits a_(nj) of the symbols a_(n)constituting the sequence a₁ ^(τ), using logarithms of the a prioriprobability ratios of the latter coming from the decoder (with the value0 on the first iteration) and taking account of the received sequence y₁^(τ) and an estimated (or re-evaluated) value ĥ(n) of the (equivalent)vector of the channel coefficients at the instant n.

The a posteriori probability ratios approximated over the bitsλ′(a_(nj)) can be divided into two portions using the followingequation:λ′(a _(nj))=λ_(a)(a _(nj))+λ_(e)(a _(nj))   (32)

After de-interleaving by the de-interleaver 14, the complete sequence ofextrinsic probability ratio logarithms becomes an intrinsic probabilityratio logarithm sequence applying to the bits of the coded symbols, andwhich is applied to the decoder 15. In an analogous manner, at theoutput of the decoder 15, each a posteriori probability ratio logarithmλ(c_(nj)) applying to the coded bit can be decomposed into an a prioriportion and an extrinsic portion. The latter can be calculated bysubtracting bit by bit in the comparator 18 the logarithm λ_(a)(c_(nj))of the a priori ratio at the output of the decoder from the logarithmλ(c_(nj)) corresponding to the a posteriori ratio:λ_(e)(c _(nj))=λ(c _(nj))−λ_(a)(c _(nj))   (33)

The sequence of extrinsic probability ratio logarithms applying to thecoded bits at the output of the decoder 15 is re-interleaved by theinterleaver 17 and returned to the decoder 15 after the next detectionof N sequences of a priori probability ratio logarithms applying to thesymbol bits. By repeating this process a certain number of times, agreat increase in the signal-to-noise ratio is achieved in relation tothe data bits of the received sequences.

Because of its very regular Viterbi structure, and its good performancegiven its moderate complexity, the DDFSE detector of the device 12appears to be perfectly suitable for turbo-detection as effected in thereceiver shown in FIG. 3.

For the first iteration, the system shown in FIG. 3 operates in exactlythe same way as described with reference to FIG. 2. On the second andsubsequent iterations, equation (31) giving the branch metric ξ_(n)(b)used by the DDFSE device 12 has an additional term: $\begin{matrix}{{\xi_{n}(b)} = {{\frac{1}{2\sigma^{2}}{{y_{n} - {{{\underset{\_}{\hat{h}}}_{0}(n)}s_{n}} - {\sum\limits_{k = 1}^{v_{r}}{{{\underset{\_}{\hat{h}}}_{k}(n)}s_{n - k}}} - {\sum\limits_{k = {v_{r} + 1}}^{2L^{\prime}}{{{\underset{\_}{\hat{h}}}_{k}(n)}{\hat{s}}_{n - k}}}}}^{2}} - {\ln\quad{\Pr\left( {b = b} \right)}}}} & (34)\end{matrix}$

It is calculated only once during the recursive forward processing, andthen stored in memory.

The a priori probability logarithm In Pr(b=b) on the branch bεB_(n) inequation (31) corresponds exactly to the a priori probability logarithmof the label b^(∇) that it carries, so that:ln Pr(b=b)=ln Pr(b ^(▾) =b ^(▾))=ln Pr (a _(n) =b ^(▾))   (35)

If perfect de-correlation is assumed between the a priori probabilitylogarithms over the symbol bits a_(nj) after re-interleaving of thesequence of extrinsic probability ratio logarithms coming from the codeC₀, there is obtained: $\begin{matrix}{{\ln\quad{\Pr\left( {b = b^{▼}} \right)}} = {{\sum\limits_{j = 1}^{q}{\ln\quad{\Pr\left( {b_{j}^{▼} = b_{j}^{▼}} \right)}}} = {\sum\limits_{j = 1}^{q}{\ln\quad{\Pr\left( {a_{n,j} = b_{j}^{▼}} \right)}}}}} & (36)\end{matrix}$

Finally, using equations (25) and (36), the output λ′(a_(nj)) of theDDFSE device 12 applied to the symbol a_(nj) can be decomposed into asum of two logarithmic terms:λ′(a _(nj))=λ_(a)(a _(nj))+λ′_(e)(a _(nj))   (37)where: $\begin{matrix}{{\lambda_{a}\left( a_{n,j} \right)} = {\ln\frac{\Pr\quad\left( {a_{n,j} = 1} \right)}{\Pr\quad\left( {a_{n,j} = 0} \right)}}} & (38)\end{matrix}$represents the logarithm of the a priori ratio applying to the bita_(nj) supplied by the decoder 15, and where: $\begin{matrix}{{\lambda_{e}^{\prime}\left( a_{n,j} \right)} = {{\min\limits_{{b \in B_{n}},{b_{j}^{▼} = 0}}\left\{ {{\mu_{n - 1}^{\rightarrow}\left( b^{-} \right)} + {\xi_{n}^{e,j}(b)} + {\mu_{n}^{\leftarrow}\left( b^{+} \right)}} \right\}} - {\min\limits_{{b \in B_{n}},{b_{j}^{▼} = 1}}\left\{ {{\mu_{n - 1}^{\rightarrow}\left( b^{-} \right)} + {\xi_{n}^{e,j}(b)} + {\mu_{n}^{\leftarrow}\left( b^{+} \right)}} \right\}}}} & (39)\end{matrix}$with: $\begin{matrix}{{\xi_{n}^{e,j}(b)} = {{\frac{1}{2\sigma^{2}}{{y_{n} - {{{\underset{\_}{\hat{h}}}_{0}(n)}s_{n}} - {\sum\limits_{k = 1}^{v_{r}}{{{\underset{\_}{\hat{h}}}_{k}(n)}s_{n - k}}} - {\sum\limits_{k = {v_{r} + 1}}^{2L^{\prime}}{{{\underset{\_}{\hat{h}}}_{k}(n)}{\hat{s}}_{n - k}}}}}^{2}} - {\sum\limits_{l \neq j}^{\quad}{\ln\quad{\Pr\left( {a_{n,l} = b_{l}^{▼}} \right)}}}}} & (40)\end{matrix}$the second term of this equation representing the logarithm of theextrinsic probability ratio on the bit a_(nj) resulting from all theother bits of the symbols with bit labels of the sequence a₁ ^(τ)throughout the decoding process. It must be emphasized that, ifν_(r)=2L′, the algorithm executed by the DDFSE device 12 becomesformally equivalent to a Min-Log-BCJR algorithm applied to the whole ofthe channel trellis. If the processing were applied to only a reducedstate trellis, the estimated sequences obtained from the history of thepaths and involved in the derivatives of the branch metrics woulddegrade performance because of a possible error propagation effect.Nevertheless, it appears that the equivalent channels at the output ofthe rake receiver 11 do not introduce any significant error propagationinto the structure of the DDFSE reduction device 12. As a result, thechoice ν_(r)=1 is sufficient in most cases.

The performance of conventional channel estimation obtained bycorrelation and average calculation processing applied to a sequence ofpilot symbols is degraded with low spreading values because ofintersymbol interference.

To illustrate this, FIG. 6 shows curves of bit error rate as a functionof signal-to-noise ratio at the output of the decoder 15 obtained withdifferent solutions. In this figure, the curve C6 corresponds to theideal situation. The curve C7, which corresponds to the situation inwhich a conventional channel estimation is used, shows that thissolution offers relatively poor performance, relatively far removed fromthe ideal.

The present invention proposes to improve the quality of channelestimate in the systems shown in FIGS. 2 to 4 by using the knownstructure of the intersymbol interference. To this end, the channelestimator 10 shown in FIG. 7 is used. This channel estimator comprises aconventional channel estimator 30 whose output is connected to a channelestimate corrector 31 using the minimum mean squared error (MMSE) methodor the least squares (LS) method, delivering a channel estimate that isused by the device 12 and by the channel modeling device 16.

One example of a conventional channel estimator is described inreference [1], for example.

If it is assumed that the path delays are spaced by a multiple of thechip period T_(c) and that the spreading of the delays is less than thesymbol period T_(s), the conventional channel estimates are obtainedusing the following formula:ĥ=(ĥ ₁ , . . . ,ĥ _(L))^(T) =Mh+n   (41)in which: $\begin{matrix}{{M = \left\lbrack M_{ji} \right\rbrack_{{0 \leq j},{i \leq {L - 1}}}},} & (42) \\{{M_{ii} = 1},{0 \leq i \leq {L - 1}},} & (43) \\{{{M_{ji} = {\sum\limits_{p = 0}^{P - 1}{\frac{s_{p}^{*}}{{s_{p}}^{2}}\left\lbrack {{s_{p + {\lfloor\frac{\tau_{ji}}{N}\rfloor}}{\sum\limits_{n = p_{N}}^{{{({p + 1})}N} - 1 - \tau_{ji} + {N{\lfloor\frac{\tau_{ji}}{N}\rfloor}}}{{\mathbb{e}}_{n}^{*}{\mathbb{e}}_{n + \tau_{ji}}}}} + {s_{p + {\lfloor\frac{\tau_{ji}}{N}\rfloor} + 1}{\sum\limits_{n = {{{({P + 1})}N} - \tau_{ji} + {N{\lfloor\frac{\tau_{ji}}{N}\rfloor}}}}^{{{({p + 1})}N} - 1}{{\mathbb{e}}_{n}^{*}{\mathbb{e}}_{n + \tau_{ji}}}}}} \right\rbrack}}},{{{if}\quad\tau_{j}} > {\tau_{i}\quad{and}}}}\text{}} & (44) \\{{M_{ji} = {\sum\limits_{p = 0}^{P - 1}{\frac{s_{p}^{*}}{{s_{p}}^{2}}\left\lbrack {{s_{p + {\lfloor\frac{\tau_{ji}}{N}\rfloor} + 1}{\sum\limits_{n = {{pN} - \tau_{ji} + {N{\lfloor\frac{\tau_{ji}}{N}\rfloor}}}}^{{{({p + 1})}N} - 1}{{\mathbb{e}}_{n}^{*}{\mathbb{e}}_{n + \tau_{ji}}}}} + {s_{p + {\lfloor\frac{\tau_{ji}}{N}\rfloor}}{\sum\limits_{n = {pN}}^{{pN} - 1 - \tau_{ji} + {N{\lfloor\frac{\tau_{ji}}{N}\rfloor}}}{{\mathbb{e}}_{n}^{*}{\mathbb{e}}_{n + \tau_{ji}}}}}} \right\rbrack}}},{{{if}\quad\tau_{j}} < \tau_{i}}} & (45)\end{matrix}$where p is the number of symbols in the pilot sequence, h=(h₁, . . . ,h_(L))^(T) being the perfect (noiseless) channel coefficients, n beingthe channel noise estimate that is assumed to have a variance equal toN₀/E_(pilot), and E_(pilot)is the pilot symbol energy. MMSE channelestimate is deduced from the conventional channel estimate using thefollowing formula:ĥ ^(MMSE) =L ^(H) ĥ  (46)in whichL=argmin∥ĥ ^(MMSE) −h∥  (47)

Using equation (18), it can be deduced that: $\begin{matrix}{{\hat{h}}^{MMSE} = {{M^{H}\left( {{MM}^{H} + {\frac{N_{0}}{E_{{call}\quad{director}}}I_{L}}} \right)}^{- 1}\hat{h}}} & (48)\end{matrix}$in which M^(H) is the conjugate transposed matrix of the matrix M.

An estimate can also be effected using the LS method:ĥ ^(LS=() M ^(H) M)⁻¹ M ^(H) ĥ.   (49)

However, this estimation method does not take account of the noise powerand therefore degrades performance in terms of signal-to-noise ratiocompared to MMSE estimation. As is apparent in FIG. 6, the simulationsshow that a conventional channel estimate, based only on correlationwith pilot symbols, leads to poor performance with a low spreadingfactor (curve C7). The structure of the intersymbol interference must betaken into account, which is possible using the MMSE and LS methods. Theperformance achieved using the MMSE method is shown by the curve C9 inFIG. 6, which indicates a significant improvement over conventionalmethods.

In an advantageous variant of the invention shown in FIG. 4, thereceiver of the invention comprises an iterative detection loop foreffecting channel re-estimation. The loop comprises an interleaver 19connected to the output of the decoder 15 and whose output is connectedvia a threshold comparator 20 to an iterative channel estimation device21. The comparator 20 transforms the weighted or flexible output of thedecoder 15 into a “hard” output, i.e. one that is equal to 0 or to 1,depending on whether the weighted value is greater than a predeterminedthreshold or not.

What is more, the signal applied at the input of the rake receiver 11 isalso applied to the iterative channel estimation device 21 and to achannel estimation device 10 used to effect a first channel estimationusing the pilot sequence, these devices supplying channel estimates byway of respective switches 23, 24 to the channel modeling device 16designed to determine an equivalent channel model that is applied to theinput of the DDFSE device 12.

The receiver effects a first decoding on the basis of the channelestimates determined by the device 10 using pilot symbols and applied tothe channel modeling device 16 (switches 23, 24 respectively closed andopen). The estimated codes coming from the channel decoder 15 are thenused by the device 21 to re-estimate the channel, in order to reduce thechannel estimation noise for the next iteration, the channel estimatesdetermined in this way being applied to the modeling device 16 (switches23, 24 respectively open and closed).

Of course, the iterative channel estimation process shown in FIG. 4 canalso be applied to the system shown in FIG. 2, i.e. to a receiver thatdoes not include the iterative detection loop shown in FIG. 3.

The curves C8 and C10 in FIG. 6 show the performance achieved using thesolution shown in FIG. 4 employing an iterative channel estimationmethod, the curve C8 corresponding to the situation in which aconventional channel estimator is used and the curve C10 to a channelestimator with MMSE correction. These two curves show that this methodimproves performance and, in the case of channel estimation with MMSEcorrection, approximates the ideal situation shown by the curve C6.

1. A method for receiving a signal transmitted on a multipathtransmission channel using a spread spectrum technique with a lowspreading factor, said signal being transmitted in the form of sequencesof coded binary symbols comprising predefined pilot symbols and datasymbols, multiplied by a spreading sequence, said method including astep of determining a channel estimate using received predefined pilotsymbols, the method comprising the steps of: reconstituting thetransmitted signal using the channel estimate on the basis of receivedsignals that were transmitted over the multiple paths of thetransmission channel, determining from the channel estimate anequivalent model of the transmission channel as seen from thereconstituted signal, reducing intersymbol interference in thereconstituted transmitted signal resulting from a low spreading factor,using the channel equivalent model and a DDFSE detector based on atrellis with a reduced number of states and delivering at its outputestimated values of the received coded symbols, de-interleaving thecoded symbols, and decoding the estimated values of the de-interleavedcoded symbols to reconstitute the transmitted data symbols.
 2. Themethod according to claim 1, wherein the estimated values of thereceived coded symbols obtained after interference reduction anddecoding are weighted or flexible values.
 3. The method according toclaim 2, wherein the steps of reducing intersymbol interference anddecoding are included in an iterative process wherein the coded andde-interleaved symbols obtained in an iteration n are re-estimatedduring decoding as a function of the data symbols obtained afterdecoding and error correction, the difference between the re-estimatedcoded symbols obtained in the same iteration and the de-interleavedcoded symbols obtained in the next iteration n+1 is re-interleaved andthen applied to the input of the DDFSE detector and subtracted from thecoded symbols obtained in the iteration n+1 at the output of the DDFSEdetector.
 4. The method according to claim 1, wherein the channelestimate is improved using a least squares (LS) method.
 5. The methodaccording to claim 1, wherein the channel estimate is improved using aminimum mean square error (MMSE) algorithm.
 6. The method according toclaim 1, wherein the channel is estimated in an iterative process andthe coded and de-interleaved symbols obtained in an iteration n arere-estimated during decoding as a function of the data symbols obtainedafter decoding and error correction, the re-estimated coded symbols areinterleaved, a channel estimate is obtained on the basis of there-estimated and interleaved coded symbols, an equivalent channel modelis determined from the channel estimate, and the channel estimate andthe channel equivalent model determined in an iteration n arerespectively used to reconstitute the transmitted signal and to reduceintersymbol interference in the next iteration n+1.
 7. A system forreceiving a signal transmitted on a multipath transmission channel usinga spread spectrum technique and a low spreading factor, said signalbeing transmitted in the form of a sequence of coded binary symbolscomprising predefined pilot symbols and data symbols and multiplied by aspreading sequence, said system comprising a rake receiver forreconstituting the transmitted signal using a channel estimate on thebasis of the signals received and transmitted by the multiple paths ofthe transmission channel and first channel estimation means forestimating the channel on the basis of the pilot symbols received, todeliver a transmission channel estimate to the rake receiver, whereinthe system further comprises: channel modeling means for determining anequivalent model of the channel as seen at the output of the rakereceiver as a function of the channel estimate, reduction means forreducing intersymbol interference between received symbols, the reducingmeans comprising a DDFSE detector based on a trellis with a reducednumber of states, for reducing intersymbol interference between receivedsymbols using the equivalent channel model and reconstituting estimatedvalues of the coded symbols received, de-interleaving means forde-interleaving the estimated values of the coded symbols received, anddecoding means for decoding the estimated and de-interleaved values andsupplying the data symbols transmitted.
 8. The reception systemaccording to claim 7, wherein the estimated and de-coded valuesdelivered by the intersymbol interference reduction means and thedecoding means are weighted or flexible values.
 9. The reception systemaccording to claim 7, further comprising: means for re-estimating thecoded symbols as a function of the decoded data symbols after errorcorrection, first subtraction means for subtracting the estimated andde-interleaved coded symbols from the re-estimated coded symbols andobtaining a sequence of extrinsic re-estimated coded symbols, firstinterleaving means for interleaving the sequence of extrinsicre-estimated coded symbols, and second subtraction means for subtractingthe sequence of extrinsic re-estimated coded symbols from the sequenceof symbols received and estimated by the reduction means on the nextiteration.
 10. The reception system according to claim 9, furthercomprising: second interleaving means t for interleaving the sequence ofre-estimated coded symbols at the output of the decoding means, andsecond channel estimation means for supplying a transmission channelestimate on the basis of the interleaved sequence of re-estimated codedsymbols to the means for determining an equivalent channel model and tothe rake receiver.